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Diffusion through semipermeable structures arises in a wide range of processes in the physical and life sciences. Examples at the microscopic level range from artificial membranes for reverse osmosis to lipid bilayers regulating molecular…

Statistical Mechanics · Physics 2023-01-11 Paul C Bressloff

We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node…

Machine Learning · Computer Science 2021-01-20 Shushan He , Hongyuan Zha , Xiaojing Ye

This paper studies probability density evolution for stochastic hybrid systems with reset maps that change the dimension of the continuous state across modes. Existing Frobenius--Perron formulations typically represent reset-induced…

Optimization and Control · Mathematics 2026-04-09 Tejaswi K. C. , Taeyoung Lee

We give here a covariant definition of the path integral formalism for the Lagrangian, which leaves a freedom to choose anyone of many possible quantum systems that correspond to the same classical limit without adding new potential terms…

High Energy Physics - Theory · Physics 2009-09-25 Andres Jordan , Matias Libedinsky

We consider a Brownian particle moving on a ring. We study the probability distributions of the total number of turns and the net number of counter-clockwise turns the particle makes till time t. Using a method based on the renewal…

Statistical Mechanics · Physics 2014-11-03 Anupam Kundu , Alain Comtet , Satya N. Majumdar

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE…

Machine Learning · Computer Science 2021-11-03 Xiang Huang , Hongsheng Liu , Beiji Shi , Zidong Wang , Kang Yang , Yang Li , Bingya Weng , Min Wang , Haotian Chu , Jing Zhou , Fan Yu , Bei Hua , Lei Chen , Bin Dong

We propose a new Neural Galerkin Normalizing Flow framework to approximate the transition probability density function of a diffusion process by solving the corresponding Fokker-Planck equation with an atomic initial distribution,…

Machine Learning · Computer Science 2026-03-20 Riccardo Saporiti , Fabio Nobile

Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks. The probability distribution is encoded into a Boolean normal form and compiled to a target language,…

Artificial Intelligence · Computer Science 2016-10-19 Giso H. Dal , Peter J. F. Lucas

We have formulated higher-order integration by parts formulae on the path space restricted between two curves, with respect to pinned/ordinary Wiener measures. The higher-order integration by parts formulae introduce nontrivial boundary…

Probability · Mathematics 2024-05-10 Kensuke Ishitani , Soma Nishino

The role of classical noise in quantum walks (QW) on integers is investigated in the form of discrete dichotomic random variable affecting its reshuffling matrix parametrized as a SU2)/U(1) coset element. Analysis in terms of quantum…

Quantum Physics · Physics 2015-06-05 D. Ellinas , A. J. Bracken , I. Smyrnakis

A generalization is provided for the notion of tags, as used in various formulations of physical scenarios. It leads to the definition of tagged vector spaces, based on a set of axioms for tags and their extractors. As an application, such…

Quantum Physics · Physics 2025-10-21 Filippus S. Roux

Dirichlet processes and their extensions have reached a great popularity in Bayesian nonparametric statistics. They have also been introduced for spatial and spatio-temporal data, as a tool to analyze and predict surfaces. A popular…

Statistics Theory · Mathematics 2023-03-31 Clara Grazian

Starting with the first-order singular Lagrangian, the canonical structures of the noncommutative quantum system on a submanifold embedded in the higher-dimensional Euclidean space are investigated with the projection operator method (POM)…

High Energy Physics - Theory · Physics 2015-03-24 M. Nakamura

Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity of data. Many works focus on adjusting the distribution…

Artificial Intelligence · Computer Science 2024-12-16 Xiang Huang , Hao Peng , Li Sun , Hui Lin , Chunyang Liu , Jiang Cao , Philip S. Yu

In this paper, we propose a novel algebraic and geometric description for the dissipative dynamics. Our formulation bears some similarity to the Poisson structure for non-dissipative systems. We develop a canonical description for…

Classical Physics · Physics 2009-11-07 Sonnet Q H Nguyen , Lukasz A Turski

This paper is a step in the direction of understanding the behavior of non-intersecting Brownian motions on the real line, when the number of particles becomes large. Consider 2k non-intersecting Brownian motions, all starting at the…

Probability · Mathematics 2007-05-23 Mark Adler , Pierre van Moerbeke

In this paper we explore the idea of looking at the Dirac quantisation conditions as $\hbar$-dependent constraints on the tangent bundle to phase-space. Starting from the path-integral version of classical mechanics and using the natural…

dg-ga · Mathematics 2016-08-31 Ennio Gozzi

A discrete formulation of the real-time path integral as the expectation value of a functional of paths with respect to a complex probability on a sample space of discrete valued paths is explored. The formulation in terms of complex…

Quantum Physics · Physics 2024-06-06 Wayne Polyzou

We introduced a new formulation for the path integral formalism for a noncommutative (NC) quantum mechanics defined in the recently developed Doplicher-Fredenhagen-Roberts-Amorim (DFRA) NC framework that can be considered an alternative…

High Energy Physics - Theory · Physics 2012-06-20 Mario J. Neves , Everton M. C. Abreu

We propose diffusion-like equations with time and space fractional derivatives of the distributed order for the kinetic description of anomalous diffusion and relaxation phenomena, whose diffusion exponent varies with time and which,…

Statistical Mechanics · Physics 2009-11-07 A. V. Chechkin , R. Gorenflo , I. M. Sokolov
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